1. Technical Field
The present invention relates to a flow control within communication networks and more particularly, to flow control in distributed networks.
2. Discussion of the Related Art
In complex distributed network infrastructures, a massive amount of data is aimed to be transmitted from sources to destinations (aka sinks) at a rate that often exceeds the capacity and the limited resources of the networking infrastructure. Generally, such a problem may occur in any dynamic network setting where congestion may occur due to temporary excess load in different parts of the network.
Excess load is likely to occur in an advanced metering infrastructure (AMI), which is a system that measures, collects and analyzes data originated from advanced metering devices. Advanced metering devices transmit measurements to some central utility for monitoring and control purposes. Such devices are massively deployed so as to cover geographical regions of interest, and are commonly equipped with sensors, transmitting measurements both upon request and on a regular schedule. In such systems, a massive amount of data is produced and transmitted periodically.
An opposite example with respect to the flow direction of data might be a multicast transmission between different domains. One of the main questions in these infrastructures is how to deal with the massive amount of data to be transmitted online, in a way that both ensures the transmission of data with a high quality level and minimizes end-to-end delivery times.
Two main well-known approaches for dealing with such problems are flow control and volume reduction. Flow control is the process of managing the rate of data transmission between two nodes to prevent the sender from overwhelming the receiver. Rates are decreased by delaying transmission of messages, resulting in a transmission slowdown and in an increase of the end-to-end delay. Volume reduction consists of performing some operations on the data so as to decrease its total amount. Existing volume reduction methods are commonly either generic algorithms or application-specific discarding policies. While the existing approaches may alleviate the problem to a certain degree, none can solve it entirely in a sufficiently wide range of scenarios and applications.
The state of the infrastructures in the aforementioned networks evolves dynamically during the system operation, both in terms of rates and the nature of data to be transmitted. An important challenge is to cope with the congestion problem in a distributed way, exploiting the resources in the whole networking infrastructure.